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171 lines
7.3 KiB
171 lines
7.3 KiB
4 days ago
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# Copyright 2023 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Processor class for Llava.
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"""
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import numpy as np
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from ...feature_extraction_utils import BatchFeature
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from ...image_utils import ImageInput, get_image_size, to_numpy_array
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from ...processing_utils import (
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MultiModalData,
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ProcessingKwargs,
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ProcessorMixin,
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Unpack,
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)
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from ...tokenization_utils_base import PreTokenizedInput, TextInput
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from ...utils import auto_docstring, logging
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logger = logging.get_logger(__name__)
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class LlavaProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {
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"text_kwargs": {"padding": False, "return_mm_token_type_ids": False},
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}
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@auto_docstring
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class LlavaProcessor(ProcessorMixin):
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def __init__(
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self,
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image_processor=None,
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tokenizer=None,
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patch_size=None,
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vision_feature_select_strategy=None,
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chat_template=None,
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image_token="<image>", # set the default and let users change if they have peculiar special tokens in rare cases
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num_additional_image_tokens=0,
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**kwargs,
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):
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r"""
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patch_size (`int`, *optional*):
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Patch size from the vision tower.
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vision_feature_select_strategy (`str`, *optional*):
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The feature selection strategy used to select the vision feature from the vision backbone.
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Should be same as in model's config
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image_token (`str`, *optional*, defaults to `"<image>"`):
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Special token used to denote image location.
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num_additional_image_tokens (`int`, *optional*, defaults to 0):
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Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other
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extra tokens appended, no need to set this arg.
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"""
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self.patch_size = patch_size
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self.num_additional_image_tokens = num_additional_image_tokens
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self.vision_feature_select_strategy = vision_feature_select_strategy
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self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
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self.image_token_id = tokenizer.encode(self.image_token, add_special_tokens=False)[0]
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super().__init__(image_processor, tokenizer, chat_template=chat_template)
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@auto_docstring
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def __call__(
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self,
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images: ImageInput | None = None,
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text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
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**kwargs: Unpack[LlavaProcessorKwargs],
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) -> BatchFeature:
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r"""
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Returns:
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[`BatchFeature`]: A [`BatchFeature`] with the following fields:
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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`None`).
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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"""
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if images is None and text is None:
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raise ValueError("You have to specify at least one of `images` or `text`.")
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output_kwargs = self._merge_kwargs(
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LlavaProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if images is not None:
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image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
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else:
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image_inputs = {}
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if isinstance(text, str):
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text = [text]
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elif not isinstance(text, list) and not isinstance(text[0], str):
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raise TypeError("Invalid input text. Please provide a string, or a list of strings")
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# try to expand inputs in processing if we have the necessary parts
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prompt_strings = text
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if image_inputs.get("pixel_values") is not None:
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# Replace the image token with the expanded image token sequence
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pixel_values = image_inputs["pixel_values"]
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height, width = get_image_size(to_numpy_array(pixel_values[0]))
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num_image_tokens = (height // self.patch_size) * (
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width // self.patch_size
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) + self.num_additional_image_tokens
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if self.vision_feature_select_strategy == "default":
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num_image_tokens -= 1
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prompt_strings = []
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for sample in text:
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sample = sample.replace(self.image_token, self.image_token * num_image_tokens)
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prompt_strings.append(sample)
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return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
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return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
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text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
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self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
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if return_mm_token_type_ids:
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array_ids = np.array(text_inputs["input_ids"])
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mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
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mm_token_type_ids[array_ids == self.image_token_id] = 1
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text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
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return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
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def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
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"""
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Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
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Args:
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image_sizes (`list[list[int]]`, *optional*):
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The input sizes formatted as (height, width) per each image.
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Returns:
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`MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
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input modalities, along with other useful data.
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"""
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vision_data = {}
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if image_sizes is not None:
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images_kwargs = LlavaProcessorKwargs._defaults.get("images_kwargs", {})
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images_kwargs.update(kwargs)
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crop_size = images_kwargs.get("crop_size", None) or self.image_processor.crop_size
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resized_height, resized_width = crop_size["height"], crop_size["width"]
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num_image_tokens = (resized_height // self.patch_size) * (resized_width // self.patch_size)
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num_image_tokens += self.num_additional_image_tokens
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if self.vision_feature_select_strategy == "default":
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num_image_tokens -= 1
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num_image_tokens = [num_image_tokens] * len(image_sizes)
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num_image_patches = [1] * len(image_sizes)
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vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
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return MultiModalData(**vision_data)
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__all__ = ["LlavaProcessor"]
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